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Paper Citation Record · LEDGER

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2412.00241.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00241 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:43:50.298140Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T14:04:13.095632Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact1
  • verified fuzzy17
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dcf07aba-a2f4-4ee0-aff1-6c8b6926c5af · outbound

This paper cites The Surprising Power of Graph Neural Networks with Random Node Initialization.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations The Surprising Power of Graph Neural Networks with Random Node Initialization

Reference 1

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unresolved
no resolver link, observed 2026-08-12T05:43:50.118201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.118201Z digest=sha256:54df931b89f659a44a1a037da27cc6d1ebb663cac2de1925b8557790e441cd28

Observation 13fa6b79-4b64-462d-89b1-6b38e0e32593 · outbound

This paper cites Realistic synthetic financial transactions for anti-money laundering models.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Realistic synthetic financial transactions for anti-money laundering models

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.042904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.123803Z digest=sha256:e3de2cfa405853749a6bb2bcd56b5ba9d00ea355882d5cdabd1e20af47b91989

Observation 7a657842-78fe-41de-aff1-e32b3a1101aa · outbound

This paper cites Graph neural networks with local graph parameters.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Graph neural networks with local graph parameters

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.027602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.129285Z digest=sha256:3f75cc99ec7966504b043035bbf3e61fbe0175ced20e658b1d6ebceea3d3203d

Observation 201331df-b844-41ca-9600-b7a736a09f44 · outbound

This paper cites Bronstein, and Haggai Maron.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein, and Haggai Maron

Reference 4

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unresolved
no resolver link, observed 2026-08-12T05:43:50.134468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.134468Z digest=sha256:c170f86109479857d4837ad275795ea28ffe26e005b6dd2aa1415962449c6f48

Observation d3e2e8f2-40c1-4d58-8156-6ede2dda8b96 · outbound

This paper cites Efficient subgraph GNN s by learning effective selection policies.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Efficient subgraph GNN s by learning effective selection policies

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:51.003386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.138995Z digest=sha256:cf4e510c1d3867f588c7a4e7c96c5878b4e562cdc38cf292eb56d9584cc5e61d

Observation dc8c5a24-bb4d-4c43-9497-1003945e48ae · outbound

This paper cites Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.144064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.144064Z digest=sha256:c3a5456693c9dc3183a78af831bc4e4463a1328f7d0f5e995e37a34488362ff7

Observation 34831829-3697-4819-87d2-0a1a72c1d2e6 · outbound

This paper cites Bronstein.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.149982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.149982Z digest=sha256:1f5dd05a4a9690aa3cf2549ecdf9602740317644d5420c08084ec1de22aaa996

Observation dde4ec97-27c2-47d3-870a-803fec23218c · outbound

This paper cites Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

Reference 8

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unresolved
no resolver link, observed 2026-08-12T05:43:50.154010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.154010Z digest=sha256:bf8ac66642f50bc2e6f54b26d44287dfa9f3cb99f12d03406e6bce7277f2ced6

Observation 08f70b30-89d1-4ca2-8dc1-44c6e2bcb10b · outbound

This paper cites Phishing scams detection in Ethereum transaction network.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Phishing scams detection in Ethereum transaction network

Reference 9

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unresolved
no resolver link, observed 2026-08-12T05:43:50.158427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.158427Z digest=sha256:bc57d3de70f0cce911b18ba80243331fc70e95643cf0a2483e6a0c5623580b9c

Observation 76bc6506-dedb-43f9-b6da-4febdba36b6c · outbound

This paper cites Principal neighbourhood aggregation for graph nets.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Principal neighbourhood aggregation for graph nets

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.988362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.162700Z digest=sha256:a2996ff830abe27368c0630ee2aaa0760316216c91a2d526604f6812abe966b5

Observation 39c58762-16b6-47d0-8ec6-d45c1aed97d1 · outbound

This paper cites Provably powerful graph neural networks for directed multigraphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Provably powerful graph neural networks for directed multigraphs

Reference 11

Resolution
verified exact
doi, observed 2026-08-12T05:43:50.363023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.167512Z digest=sha256:fb0e2ae94e396d24e0b0befc0bca9a6349718e8c783fe8b71376ef6ec0ec777c

Observation 34ebc343-d6c6-4091-9c00-fe0e2b16977e · outbound

This paper cites Hypergraph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Hypergraph neural networks

Reference 12

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unresolved
no resolver link, observed 2026-08-12T05:43:50.172473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.172473Z digest=sha256:1303e7e946b44419797a3c23ba9a295e177364fa28ed2fd6a1fb2e2ceeefce0d

Observation 7df3206e-37be-4fb1-9f54-c3f9141f0dfb · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-12T05:43:50.177350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.177350Z digest=sha256:7495278fd27cf54c10c48c8d0ff13063a13a9eff70019b1d597c2f9a48673220

Observation 5464658d-c638-4ffa-8571-ab2857c013c0 · outbound

This paper cites Bronstein, and Haggai Maron.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Bronstein, and Haggai Maron

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.961537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.181938Z digest=sha256:0203d19570f33f7062d7925a8e9d56586b445d62ac002ca2ffdb6bae243826b1

Observation b1f7b0ec-7e1c-417e-8c92-1c146a18e94d · outbound

This paper cites Fuchs* and Petar Veličković*.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Fuchs* and Petar Veličković*

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.946023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.186192Z digest=sha256:1d4a58cc21a04c396dc9de65694fd3f757b4678588558fe592d16350fe19eb13

Observation b82e6254-a25d-4d0d-a5a1-e1e7d2708196 · outbound

This paper cites Schoenholz, Patrick F.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Schoenholz, Patrick F

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.932349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.190758Z digest=sha256:4f1676e86fb3a958c29538b7068546adec1add11bf702ecd6c0a85ed837f5ca8

Observation 243e1076-f477-404f-99b1-5ef9e7fb0b7f · outbound

This paper cites Inductive representation learning on large graphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Inductive representation learning on large graphs

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.918087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.195120Z digest=sha256:e0ae57cde73894a53ffec326a20570ca2918b183f55267ef670a3a5bdfc751fc

Observation d3f186bf-126a-43a0-89dc-fc42d2bd4429 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Multilayer feedforward networks are universal approximators

Reference 18

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unresolved
no resolver link, observed 2026-08-12T05:43:50.199845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.199845Z digest=sha256:74255155def355ab198ef4f100498cbac02c45f118658bd44b8fae547dbe56f7

Observation 9e4ade6a-e0da-4aeb-8605-354d50fc0583 · outbound

This paper cites Unignn: a unified framework for graph and hypergraph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unignn: a unified framework for graph and hypergraph neural networks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.204465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.204465Z digest=sha256:ac6ffc9095771619ddfc1c23cf835f6363b018238e3f0b2ccb7820c5f818c8a0

Observation 507e9190-8125-4344-be69-739d1bf56676 · outbound

This paper cites edGNN: a Simple and Powerful GNN for Directed Labeled Graphs.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations edGNN: a Simple and Powerful GNN for Directed Labeled Graphs

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.209457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.209457Z digest=sha256:3b3213cf83d8c91f5d2641b740abfcac1b9f9bb1695d8f427b3cc4de23767cb9

Observation 6f04dbfd-0315-47e9-964e-9bbcda679e55 · outbound

This paper cites Universal invariant and equivariant graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Universal invariant and equivariant graph neural networks

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.903841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.214525Z digest=sha256:43869d95d150453a52926178c040b260bfa67828d2d0bbb2680822d4b0bb7940

Observation 8f5a688d-ffaf-40da-8d23-62ca1d96e399 · outbound

This paper cites Kipf and Max Welling.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Kipf and Max Welling

Reference 22

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unresolved
no resolver link, observed 2026-08-12T05:43:50.219052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.219052Z digest=sha256:8f651b49c88aafef0cf493b26726020877214b990e26b01c7f7fc5e16b5fb611

Observation 11f0d29a-a11d-401b-830a-3a619919b24f · outbound

This paper cites Generalised f-mean aggregation for graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Generalised f-mean aggregation for graph neural networks

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.877980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.223700Z digest=sha256:8cac22417d67ba7b94103a277290d5ec7315ea7280f750a56eb61e88c43e0d5e

Observation 70110522-4287-47bb-a2a5-d1f358f0143b · outbound

This paper cites What graph neural networks cannot learn: depth vs width.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations What graph neural networks cannot learn: depth vs width

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.863147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.228251Z digest=sha256:415125af81161382543dad48b5f1525db4fe9921a678621b479e1e88e278a4e4

Observation e4600b10-0580-448f-a8e3-123591c66763 · outbound

This paper cites Provably powerful graph networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Provably powerful graph networks

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.847784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.232920Z digest=sha256:b0df92a4b0175d6dc9f4cc7f4bf2f446e61b1992173c15e3cd38f999c899615a

Observation 5edeb333-d507-4931-8c6a-f614b810ef35 · outbound

This paper cites Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.237438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.237438Z digest=sha256:ac04bed6c64e4918e7e32e396fe4721f5f4b2d7b3c374b956f3a8a6bfeafdcfe

Observation 421778e4-071f-45ed-b3a1-abb03be6d7a8 · outbound

This paper cites Topology (2nd edn), 2000.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Topology (2nd edn), 2000

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.833193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.241813Z digest=sha256:0fcea7b54b569fa101b1f3d66d78ae890cfa127cb906b62344027753d8bb1fae

Observation a042cbed-2b93-4b1a-b71b-dfdfa28a58db · outbound

This paper cites Random features strengthen graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Random features strengthen graph neural networks

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.817039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.247240Z digest=sha256:8f8f0c6586a91471963af101593402ee516e2d6abd44114f46d02d2b902741a0

Observation b456e2f5-8a14-418a-bada-e6e02d7c236e · outbound

This paper cites Modeling relational data with graph convolutional networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Modeling relational data with graph convolutional networks

Reference 29

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unresolved
no resolver link, observed 2026-08-12T05:43:50.251626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.251626Z digest=sha256:355bfa9829f0b9b6a7d4e0c8f79c6b4b58e93e5ae569898e38634fe06d786ff0

Observation a4868c62-6f8d-4328-a40a-23627d225d9e · outbound

This paper cites Adamm: Anomaly detection of attributed multi-graphs with metadata: A unified neural network approach.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Adamm: Anomaly detection of attributed multi-graphs with metadata: A unified neural network approach

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.256301Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.256301Z digest=sha256:94d7a06db100692b8f638f799273884922dea76ee64fb0c464a82f7122ee6f94

Observation 96cecf91-36b1-45a3-909f-3481f528fd20 · outbound

This paper cites Composition-based multi-relational graph convolutional networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Composition-based multi-relational graph convolutional networks

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.792909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.261257Z digest=sha256:11c58b89d78b4b948b8d2ff6f3d46ad39de46990e5efff9135793fe838a64913

Observation b5f6ada4-93c2-4f46-b321-4a81e2e5fda2 · outbound

This paper cites Veličković, G.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Veličković, G

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.777713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.265775Z digest=sha256:7f458dc5c11fc849b0ba64b0b5455d9129e0c2aefe648369d469c9f51f3f91de

Observation b8bc98f9-df14-4d8a-b9cd-d409a438f8ae · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations, 2019.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations How powerful are graph neural networks? In International Conference on Learning Representations, 2019

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.270366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.270366Z digest=sha256:7ebfc3374bafb8732a68ec8f6f5f7998fc522997931c500726b91135a262c5d5

Observation fc1706ee-d7d0-4de4-8926-989b562cca91 · outbound

This paper cites Identity-aware graph neural networks.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Identity-aware graph neural networks

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T05:43:50.752738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-12T05:43:50.274717Z digest=sha256:4532defa3f24fb3cee6e7e63493a8ebb4789c66706c4355bf78f4f1ed282bb09

Observation ca16123b-a927-4b56-859e-3a9b3ce19f72 · outbound

This paper cites Deep sets.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Deep sets

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.278985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.278985Z digest=sha256:fed9fcbbc97647429a6a6bec9dd05bcdca6191a69ac94009fd6a2de391b5c122

Observation dbf98188-73c6-4a08-9e75-d1f1790f90e1 · outbound

This paper cites write newline.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations write newline

Reference 36

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unresolved
no resolver link, observed 2026-08-12T05:43:50.283678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.283678Z digest=sha256:ea47c2dcb600a6181789ef85e4810429339e16365ae28f5c54cd891a521ee697

Observation 9c583442-fc0d-410a-9b1f-d559e0480c7a · outbound

This paper cites @esa (Ref.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations @esa (Ref

Reference 37

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unresolved
no resolver link, observed 2026-08-12T05:43:50.289213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.289213Z digest=sha256:009b90fb4b852aef1e16b0583da586af1e1a5e734cff045b9ec369e8ce9b7eae

Observation 984d6786-f215-4681-9d5a-e1c850fce86b · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.293810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.293810Z digest=sha256:a274221a223b9a106ef19dc01db0580a3edce79bb627bdc62583e6e10aa65110

Observation 194b724c-6337-47db-bd97-4614601a5e29 · outbound

This paper cites an unresolved cited work.

Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-12T05:43:50.298140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:43:50.298140Z digest=sha256:ac58cb3bf17464f6faddc428ad0dfbae37fb3a84de228bbb9aa10209c71c6eb7

Pith citing papers

Observation 66c1449e-629d-4513-813a-1b2371fe88db · inbound

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention cites this paper.

SALT-GNN: Handling Dense Neighborhoods in Anti-Money Laundering Graphs via Statistics-Aware Attention Multigraph Message Passing with Bi-Directional Multi-Edge Aggregations

Reference 85

Resolution
unresolved
no resolver link, observed 2026-07-14T14:04:13.095632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:04:13.095632Z digest=sha256:317401755ed948e2e89b001a6452c5e07769c53e2178a1ab56c5d0b2f13c946b